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This research studies the impact of introducing distributed generators (DGs) into a distribution network. The aim of this paper is to optimally site DGs based on economic, environmental and reliability indices are presented.

The considered network was modelled by using the network’s line parameters and capacity of the load bus with the help of Power System Analysis Toolbox. The location of the DG is based on voltage stability index and power loss reduction index. The DG energy sources considered are the diesel generator, solar photo-voltaic (PV) and wind generator, and the objectives were to minimize cumulative cost while maximizing reliability of the network. The Advanced Interactive Multidimensional Modelling System was used for the mathematical modelling.

The obtained results in the cases of introducing renewable energy into a network improves network performance. The benefits of renewable energy on the distribution network measured in terms of electricity production cost, gas emission cost, fuel cost and value of energy not supplied were positive. The research also showed that the total benefit of renewable energy reduces as the price of the renewable generators increases.

This paper introduces a new approach to determining the optimal location of DG for reducing line losses and improved voltage profile. A new cost modelling function based on external grid power transfer cost, technical losses and cost because of the various energies source is also introduced.

The purpose of this study is to address the efficiency of power losses representation while still reducing the computational burden of an optimal power flow (OPF) model in transmission expansion planning (TEP) studies.

A modified TEP model is formulated with inclusions of linearized approximation of power losses for a large-scale power system with renewable energy sources. The multi-objectives function determines the effect of transmission line losses on the optimal power generation dispatch in the power system with and without inclusion of renewable energy sources with emphasis on minimizing the investment and operation costs, emission and the power losses.

This study investigates the impact of renewable energy sources on system operating characteristics such as transmission power losses and voltage profile. Sensitivity analysis of the performance for the developed deterministic quadratic programming models was analyzed based on optimal generated power and losses on the system.

In the future, a comparison of the alternating current OPF and direct current (DC) OPF models based on the proposed mathematical formulations can be carried out to determine the efficiency and reduction of computation process of the two models.

This paper proposed an accurate way of computing transmission losses in DC OPF for a TEP context with a view of achieving a minimal computation time.

This paper addresses the following objectives: develop a modified DC OPF with a linearized approximation of power losses in TEP problem with large integration of RES. Investigate the impact of RES on system operating characteristics such as transmission power losses and voltage profile.

Reverse osmosis (RO) has become an important method of desalination to meet the ever-growing water needs around the world. Its integration with renewable energy source (RES) reduces the environmental impact of gas emissions and cost of conventional fossil energy sources. The optimal sizing of energy sources to power RO desalination system is intended mainly to minimize the annualized cost of the system and by extension minimize freshwater cost while maximizing production.

In this study, a mathematical optimization approach is used to determine the optimal energy mix, which includes grid power, diesel generator and a photovoltaic (PV) module to supply an RO desalination unit. Three cases of optimal sizing approach were compared. Case 1 is a system with only grid power and diesel generator as energy sources; Case 2 has PV incorporated in the energy supply mix while Case 3 has the three energy sources and a Time of Use (TOU) demand response program on the demand side.

The results of implementing the optimization models show that Case 3 turnout the highest freshwater production (1,521 m³/day) at a unit cost of 1.36$/m³ when compared to Case 1 with daily freshwater production of 1,250 m³/day at a unit cost of 1.68$/m³ and Case 2 having a daily freshwater production of 1,501 m³/day at a unit cost of 1.33$/m³.

The integration of RES to power desalination system with application of TOU demand response is the significance of this study.

Storage systems are deemed to be unable to provide revenue commensurate with the resources invested in them, thus discouraging their incorporation within power networks. In prosumer microgrids, storage systems can provide revenue from reduced grid consumption, energy arbitraging or when serving as back-up power. This study aims to examine stacking these revenue streams with the aim of making storage systems financially viable for inclusion in prosumer microgrids.

With the aim of reducing self-consumption and maximising revenue, the prosumer microgrid incorporating hybrid energy storage systems (HESS) and solar PV power is solved using the CPLEX solver of the Advanced Interactive Multidimensional Modeling Software (AIMMS). The financial analysis of the results is carried out to provide the payback periods of different system configurations of the prosumer microgrid.

The findings reveal that the payback period of the three HESS when minimising grid expenses during self-consumption alone and when compared with stacked revenue streams shows an improvement from 4.8–11.2 years to 2.4–6.6 years. With stacked HESS revenues, the supercapacitor-lithium ion battery HESS gave the shortest payback period of 2.31 years when solar PV power is at 75% penetration level.

Existing literature has considered revenue streams of storage systems at the electrical power transmission and distribution levels, but not for prosumer microgrids in particular. This study has captured these benefits and verified the profitability of stacking revenue from HESS to prosumer microgrids, using a case study.

In the daily energy dispatch process in a power system, accurate short-term electricity load forecasting is a very important tool used by spot market players. It is a critical requirement for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The purpose of this study is to present an improved grey Verhulst electricity load forecasting model.

To test the effectiveness of the proposed model for short-term load forecast, studies made use of Kenya’s load demand data for the period from January 2014 to June 2019.

The convectional grey Verhulst forecasting model yielded a mean absolute percentage error of 7.82 per cent, whereas the improved model yielded much better results with an error of 2.96 per cent.

In the daily energy dispatch process in a power system, accurate short-term load forecasting is a very important tool used by spot market players. It is a critical ingredient for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The fact that the model uses actual Kenya’s utility data confirms its usefulness in the practical world for both economic planning and policy matters.

In terms of generation and transmission investments, proper load forecasting will enable utilities to make economically viable decisions. It forms a critical cog of the strategic plans for power utilities and other market players to avoid a situation of heavy stranded investment that adversely impact the final electricity prices and the other extreme scenario of expensive power shortages.

This research combined the use of natural logarithm and the exponential weighted moving average to improve the forecast accuracy of the grey Verhulst forecasting model.

This study aims to investigate the impact of the 2013 privatization of Nigeria’s energy sector on the technical performance of the Benin Electricity Distribution Company (BEDC) and its workforce.

This study used a questionnaire-based approach, and 196 participants were randomly selected. Analytical tools included standard deviation, Spearman rank correlation and regression analysis.

Before privatization, the energy sector, managed by the power holding company of Nigeria, suffered from inefficiencies in fault detection, response and billing. However, privatization improved resource utilization, replaced outdated transformers and increased operational efficiency. However, in spite of these improvements, BEDC faces challenges, including unstable voltage generation and inadequate staff welfare. This study also highlighted a lack of experience among the trained workforce in emerging electricity technologies such as the smart grid.

This study’s focus on BEDC may limit its generalizability to other energy companies. It does not delve into energy sector privatization’s broader economic and policy implications.

The positive outcomes of privatization, such as improved resource utilization and infrastructure investment, emphasize the potential benefits of private ownership and management. However, voltage generation stability and staff welfare challenges call for targeted interventions. Recommendations include investing in voltage generation enhancement, smart grid infrastructure and implementing measures to enhance employee well-being through benefit plans.

Energy sector enhancements hold positive social implications, uplifting living standards and bolstering electricity access for households and businesses.

This study contributes unique insights into privatization’s effects on BEDC, offering perspectives on preprivatization challenges and advancements. Practical recommendations aid BEDC and policymakers in boosting electricity distribution firms’ performance within the privatization context.

This paper aims to examine the role of technological Innovation in ensuring resource sustainability in the water, energy and food (WEF) nexus, as there exists a shortage of statistical research on the extent of the influence of technological Innovation on the WEF nexus.

The study used a quantitative research method, using a well-structured questionnaire to collect data from management staff in the WEF departments in South Africa. The collected data were analyzed by using mean score ranking, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) for structural equation modelling (SEM).

The findings show that the technological process of technological innovation is significant for resource sustainability. The result also showed that technological innovations directly and statistically significantly affect WEF nexus. The EFA resulted in three components of WEF nexus product innovation, WEF nexus process innovation and WEF nexus novel innovations. Furthermore, the CFA and SEM analysis reveals that six technological innovation indicators influence the sustainability of the nexus: smart water metering technology, smart metering technology, food quality monitoring technology, agricultural technology solutions, new technological design and eco-friendly WEF products.

The sustainability of these three inevitable resources for man’s survival is dependent on technological innovations, and this study has shown the major categories of innovations needed, thus establishing a pathway for engineering design.